444 results about "Feature mining" patented technology

The invention relates to the technical field of nondestructive testing, and discloses a steel structure weld defect ultrasonic detection and identification system. A data acquisition module of the system acquires an original ultrasonic signal of a steel structure welding seam through ultrasonic detection equipment and acquires geometric attribute data of the welding seam; the model construction module constructs a welding seam three-dimensional digital model based on the data; a feature extraction module performs feature mining on the three-dimensional digital model and extracts a weld defect feature index set; the difference analysis module carries out deviation calculation on the characteristic index set and a reference index set in a standard welding seam characteristic database, and an abnormal area is identified; the risk assessment module calculates a defect sensitivity index according to the abnormal region in combination with real-time environmental parameters, and assesses a defect risk level; and the report generation module formulates a detection scheme according to the defect risk level, generates a detection instruction, executes ultrasonic scanning, collects performance data and generates a defect detection report. The system has the advantages of high detection precision, high reliability, automatic and standardized process and the like.

The invention relates to the technical field of carbon emission prediction, and discloses a combined carbon emission prediction method based on multi-source heterogeneous tensor data. The method comprises the steps that multi-source carbon emission data streams such as industrial emission, traffic flow and energy consumption in a target area are collected, and a carbon emission tensor sequence with the unified space-time dimension is generated through heterogeneous tensor conversion; multi-scale space-time correlation features in the sequence are extracted through a dynamic feature fusion algorithm, and a combined prediction model containing a long-period trend prediction branch and a short-period fluctuation prediction branch is constructed. And iteratively training the model by using a historical tensor sequence until convergence, and inputting a real-time multi-source data stream to output a combined prediction result. According to the method, effective integration and deep feature mining of multi-source heterogeneous data are realized, different change rules of carbon emission are accurately captured through branching model design, the comprehensiveness and reliability of prediction are improved, and scientific reference is provided for carbon emission management and control.

The invention relates to the technical field of photovoltaic power generation, in particular to a flexible photovoltaic intelligent monitoring and management system and method based on the Internet of Things, multi-source heterogeneous data are comprehensively collected through deployed multiple types of Internet of Things sensor nodes, the data are uploaded to a cloud platform after being cleaned and standardized through edge nodes, a big data processing architecture integrated with flow and batch is adopted, and the intelligent monitoring and management system and method based on the Internet of Things are established. The method comprises the following steps: performing real-time analysis and state judgment on a real-time data stream, performing deep batch processing and feature mining on historical data, extracting high-order features such as a performance attenuation trend and an abnormal mode, fusing real-time and historical features, and realizing comprehensive scoring of a health state of a component and accurate prediction of residual life by utilizing a machine learning model. And based on an evaluation result and a preset knowledge base, automatically generating a differentiated precise operation and maintenance instruction, and issuing and executing the differentiated precise operation and maintenance instruction to form closed-loop management. According to the invention, the monitoring depth and breadth of the flexible photovoltaic system are effectively improved, the conversion from passive alarm to active predictive maintenance is realized, and the operation reliability of the system is significantly enhanced.

The invention relates to a big data-based passenger-roll transport demand prediction and ship intelligent scheduling method and system. The method comprises the steps of obtaining multi-source shipping data for a target area; inputting the multi-source shipping data into the spatial-temporal feature mining model, and predicting passenger rolling transportation demand information of the target area; acquiring ship real-time position, passenger carrying capacity, energy consumption data and port real-time operation state in the target area, and dynamically generating an optimal scheduling scheme by adopting a shipping scheduling model in combination with the predicted passenger transport demand information; the shipping scheduling model is obtained by interacting a decision scheduling model with an intelligent agent corresponding to the passenger roller transportation system and performing iterative training by adopting a reinforcement learning algorithm; and converting the optimal scheduling scheme into visual information, pushing the visual information to operation terminals of the ship and port workers so as to start corresponding shipping scheduling operation, and monitoring the execution effect of the shipping scheduling operation in real time.

The invention belongs to the technical field of electric power system information processing, and particularly relates to an electric power information analysis method based on big data, through semantic fusion of multi-source heterogeneous data and dynamic feature mining of a time sequence attention mechanism, in a load prediction scene, compared with a traditional single data source model, the electric power information analysis efficiency is improved. After the meteorological data, the user power consumption behavior data and the power grid operation data are fused, the prediction average error rate is reduced; in an equipment fault early warning scene, through multi-dimensional correlation analysis of vibration signals, oil temperature data and environmental factors, transformer latent faults can be early warned in advance, and the fault identification accuracy is improved; meanwhile, a self-adaptive modeling engine and a closed-loop feedback mechanism enable the system to have a self-evolution capability: when the power grid topology is adjusted or the new energy grid-connected proportion is changed, the model does not need to be manually retrained, and self-adaptive adaptation can be completed in two scheduling cycles through dynamic feature weight adjustment and meta-learner parameter optimization, so that the analysis performance is maintained to be stable.

The invention discloses a marine environment real-time monitoring and early warning system based on machine learning, and relates to the technical field of machine learning. Comprising the steps that an ocean multi-source sensing module collects ocean environment data in real time through a sensor and a combined collection scheme; the multi-source feature extraction module performs time domain, change rate and frequency domain feature analysis on the data to construct a unified multi-dimensional feature vector; the multi-model fusion prediction module outputs a marine environment state vector through dynamic weighting and deviation correction based on a parallel learning architecture of a deep neural network, a long-short-term memory network and a one-dimensional convolutional neural network; and the ocean risk identification and early warning module generates graded and classified early warning information through double study and judgment of a sea condition classifier and an abnormal event detector. According to the method, comprehensive acquisition, deep feature mining, high-precision prediction and accurate early warning of marine environment data are realized, the problems of low prediction precision, risk identification lag and the like in the prior art are effectively solved, and reliable guarantee is provided for marine operation safety.

The invention provides a logistics transportation optimization method and system based on a traffic logistics large model, and the method comprises the steps: carrying out the element extraction through receiving a basic scheduling instruction of a logistics transportation task, generating core element information through semantic scene analysis, and carrying out the transportation scene adaption based on the core element information. Generating a scene influence factor set in combination with a depth feature mining result of the historical transportation scene database; then, calling a pre-trained traffic logistics large model to carry out collaborative path decision on the scene influence factor set, and outputting a candidate path scheme population containing a path topological structure and cost feature distribution through interactive iteration of a path generation unit and a cost evaluation unit in the model; and performing multi-round evolution screening on the candidate path scheme population according to a preset optimization target, determining an optimal path scheme meeting transportation requirements, and generating a transportation scheduling instruction set to be pushed to a transportation execution terminal after conversion. According to the invention, the adaptability, decision-making precision and reliability in the logistics transportation optimization process can be effectively improved.

The invention relates to the technical field of data analysis, and provides a wind turbine generator health assessment method and system based on AI multi-source data fusion, which are used for realizing accurate and dynamic wind turbine generator health assessment, and the method comprises the following steps: collecting a first operation data set of a wind turbine generator through a set sensor network, carrying out multi-dimensional state feature mining to obtain a multi-dimensional state feature set; combining the feature set, utilizing a pre-constructed state deduction model to carry out state deduction iteration, introducing different initial conditions and parameter perturbation to carry out multi-path deduction, and generating a plurality of state deduction iteration results; carrying out fusion processing based on conflict resolution on a state deduction iteration result, and obtaining a state deduction fusion result in combination with the feature relevance weight matrix and a credibility evaluation mechanism; and finally, performing state evaluation based on synchronous time sequence analysis on a state deduction fusion result and a second operation data set of which the acquisition moment is later than that of the first operation data set to obtain a health state evaluation label of the wind turbine generator.

The invention discloses a wind and light output scene generation method based on depth feature mining and adaptive clustering, and the method comprises the steps: cleaning wind and light output data, carrying out the normalization processing of the data, and enabling the data to be mapped to a preset interval, so as to eliminate the dimension influence; constructing a deep convolutional feature extraction network to extract a corresponding feature map from the normalized wind and light output data; a K-Means + + algorithm is adopted to initialize a clustering center, an improved ISODATA clustering algorithm is executed based on a density threshold dynamic splitting mechanism, clustering parameters are optimized through Bayesian optimization, and a typical scene is generated. The accuracy of the wind and light output scene is remarkably improved.

The invention relates to a copper-clad plate production line collaborative regulation and control method based on digital twinning, and the method comprises the steps: constructing a multi-level data set of dynamic virtual-real mapping through full-process digital twinning modeling, process data normalization and multi-level feature tagging, achieving the high consistency of a physical production line working condition and a simulation system, and achieving the collaborative regulation and control of a copper-clad plate production line in combination with an autonomous scheduling intelligent agent. Real-time perception and joint feature mining of multi-dimensional data such as equipment load, health degree and energy consumption are realized, the real-time adaptive scheduling capability of a production line to states such as sudden load and equipment aging is improved, scheduling target weights are periodically and adaptively generated, optimal instant balance of multi-target income is realized, and the optimal real-time scheduling capability of the production line is realized by adopting an evolutionary game and swarm intelligent optimization. A cross-device optimal resource scheduling scheme under the multi-target constraint is obtained, an evolution model is continuously fed back through real-time production data, a scheduling-execution-calibration-rescheduling closed-loop mechanism is formed, and the self-learning and long-period stability capabilities of a scheduling system are remarkably improved.

The invention relates to the field of data fusion evaluation, and provides a dynamic quantitative evaluation method for an enterprise development process, which comprises the following steps of: S1, acquiring operation data and compliance data of an enterprise through a multi-source heterogeneous data interface, and executing entity association and conflict resolution operation to generate a structured basic data set; s2, extracting dynamic evaluation features of the structured basic data set through a data analysis engine, wherein the data analysis engine comprises an industry adaptive rule base and / or a deep feature mining network; s3, calculating a multi-dimensional score value of the enterprise development situation based on the dynamic evaluation features; and S4, according to a historical evaluation record, mapping the multi-dimensional score value to a preset grade interval and presenting the multi-dimensional score value. And furthermore, objective and real-time evaluation is carried out on the growth performance and the anti-risk capability of the non-listed companies.

The invention discloses a cross-view image geographic positioning method based on dynamic threshold pseudo tag self-training learning, and the method specifically comprises the following steps: introducing a difficult sample feature mining method, dynamically adjusting the loss weight of a sample according to the change of similarity, and building a dynamic difficult sample triple loss model; the method comprises the following steps: dynamically adjusting a confidence threshold value of a sample by adopting an index moving average weighting method, iteratively training and screening an unlabeled sample, namely a pseudo label, establishing a pseudo label self-training mechanism of a dynamic threshold value, mining and utilizing non-paired data, and solving the problem of high manual labeling cost; a reference image most similar to a query image is found through image retrieval, and the offset of a query position is predicted. Experiments on CVUSA and CVACT data sets show that as the distance threshold increases, the accuracy of the cross-view image geographic positioning method based on dynamic threshold pseudo tag self-training learning presents a stable rising trend, and the cross-view image geographic positioning method based on dynamic threshold pseudo tag self-training learning is superior to other methods under the same threshold condition.

The invention discloses an earth and rockfill dam illness feature mining method and system based on historical text data, and the method comprises the steps: collecting earth and rockfill dam historical illness data, constructing an earth and rockfill dam illness diagnosis text corpus set, carrying out the structured preprocessing of the corpus set, and obtaining an illness feature and danger removal measure text corpus set; generating a structured word sequence subset through a word segmentation tool; processing the structured word sequence subset by adopting an LDA topic model, determining an optimal topic number through a confusion degree curve, and outputting a final topic and a corresponding topic word; constructing a visual network graph based on the subject term co-occurrence frequency; and carrying out centrality analysis on nodes in the visual network diagram, identifying key nodes in the visual network diagram, quantitatively analyzing association rules of the danger characteristics and danger removing measures, and completing feature mining of the earth and rockfill dam danger. The problems that traditional manual diagnosis is high in subjectivity and low in utilization rate of historical engineering data are solved, and intelligent auxiliary decision making of the earth and rockfill dam danger is achieved.

The invention relates to the technical field of power grid operation and maintenance, in particular to a distributed photovoltaic performance fluctuation feature mining method, which comprises the following steps of: acquiring output parameters, environmental parameters and operation state parameters of a photovoltaic module to construct an original data set, judging and eliminating abnormal values by adopting a 3 sigma criterion in combination with trend consistency, filling missing values by improving an adjacent weighted completion algorithm, and obtaining a data set; obtaining a preprocessed data set; the data set is preprocessed through Z-score standardization processing so as to eliminate dimensional differences, the correlation degree of the environmental parameters and the photovoltaic output power is calculated through the Pearson's correlation coefficient, and key influence factors are determined; a standardized photovoltaic output power sequence and a key environment parameter sequence are used as input, and an initial time sequence fluctuation feature set of fluctuation indexes is constructed based on a sliding time window. According to the invention, through accurate and comprehensive mining of photovoltaic performance fluctuation characteristics, reliable data support is provided for photovoltaic power station operation and maintenance optimization, power prediction precision improvement and power grid scheduling decision.

The embodiment of the invention relates to the technical field of data monitoring, in particular to an intelligent factory storage monitoring method and system based on the Internet of Things, and the method comprises the steps: carrying out the data fusion processing of original storage data collected by Internet of Things equipment, and obtaining a monitoring data set containing the environment perception data and the cargo state data with the aligned timestamps; performing storage feature mining on the monitoring data set to obtain environment correlation features and cargo state evolution features of a storage space; then, a trained storage anomaly detection model is called to carry out joint anomaly detection on the two features, and a storage anomaly detection result containing an anomaly type identifier is generated; and finally, based on the abnormal type identifier and the abnormal evolution trend information in the detection result, generating a risk early warning label containing time-space positioning information, and pushing the risk early warning label to a warehouse management terminal, thereby realizing timely and accurate early warning of warehouse abnormity.

The invention discloses an online anomaly monitoring method and system for a linear movement cutting ore pulp sampler, and relates to the technical field of industrial automation, and the method comprises the steps: executing frequency band energy separation and sliding window statistical analysis on a working condition data set of the ore pulp sampler, and obtaining a multi-dimensional feature matrix; performing weight distribution and dynamic weighted aggregation on the multi-dimensional feature matrix to form a space-time analysis data packet, performing collaborative analysis on the space-time analysis data packet, and outputting a trend collaborative interaction matrix; and performing risk quantification and contribution degree distribution on the trend collaborative interaction matrix by using an entropy weight method to generate an abnormal quantification parameter, and performing confidence coefficient weighted calculation on the abnormal quantification parameter to form an abnormal probability value. According to the method, the working condition data set of the ore pulp sampler is fully fused through the sliding window statistical analysis and the entropy weight method, and meanwhile, deep feature mining and spatial relation fusion are performed through the dynamic causal atlas and the space-time convolutional neural network model, so that the reliability of anomaly monitoring is improved.

The invention discloses a metal 3D printing detection method and device based on emission spectrum and ultrasonic fusion. The method comprises the following steps: synchronously exciting and collecting full-wave band spectrum and ultrasonic signals on the surface of a sample through laser, preprocessing the signals, inputting the preprocessed signals into a multi-modal deep learning model integrated with an LANet network for feature extraction and fusion, and realizing multi-dimensional detection by using the fused features; the analysis module is used for analyzing element components and chemical defects; a double-flow time sequence network is constructed, time domain characteristics of the ultrasonic signals are analyzed in parallel to evaluate physical defects, and frequency domain sound velocity information is analyzed to evaluate residual stress; and modeling the attenuation characteristics of the ultrasonic signal by using a deep learning model, and calculating an ultrasonic attenuation coefficient to obtain probability distribution of the grain size. According to the method, the two signals are fused, the detection accuracy is improved by utilizing the strong feature mining and nonlinear mapping capability of deep learning, and multi-dimensional comprehensive evaluation of the metal 3D printing component is realized.

The invention discloses a city-level agent resource collaborative scheduling method and system, and the method comprises the steps: collecting multi-agent interaction data, carrying out the conflict feature mining, recognizing high-frequency access resources from a resource access sequence, generating a competition hotspot set, extracting an implicit dependency relationship between agents, generating a dependency transmission chain, and constructing a resource conflict relationship graph; performing hierarchical analysis on the resource conflict relation graph, positioning a conflict sink node, tracing a conflict source, determining an arbitration key point, and performing reverse priority degradation on high-strength conflict nodes; performing influence traceability evaluation on the exclusive scheduling domain to position a core agent and an auxiliary agent, and extracting load clearance and compensation capability to generate a complementary pairing relationship; and response delay difference is extracted to generate delay compensation parameters for advanced scheduling, idle time slices in an execution window are identified to perform dynamic compression, time sequence interlacing is performed according to a window scheduling table to generate a resource allocation scheme, and efficient collaborative scheduling and peak load shifting of energy resources of the city-level multi-agent are realized.

The invention discloses an intelligent engineering cost investment control system and method fusing multi-source data, and relates to the technical field of engineering cost management. The system comprises a data acquisition module, a data preprocessing module, a multi-source data fusion module, an intelligent decision module and a cost control execution module. The data acquisition module is used for extracting geometric, material, quantity, construction process and time parameters of the components from the BIM model; the data preprocessing module eliminates noise and unifies formats; the multi-source data fusion module constructs a fusion feature matrix; the intelligent decision-making module performs deep feature mining through an improved convolutional neural network and outputs an accurate cost prediction result; and the control execution module executes multi-stage regulation and control according to the deviation between the predicted cost and the target cost. Accurate prediction and dynamic control of the project cost are realized, and scientificity and effectiveness of project cost management are improved.

A method for risk assessment of a power system in extreme weather conditions considering deep feature mining for risk assessment of a power system. The risk assessment method includes: dividing the entire power system into regions based on geographical locations; combining historical wind speed data to construct a correlation model for strong wind extreme weather in multiple regions of the power system; constructing a strong wind scenario sample set for each region based on the correlation model; obtaining the probability of transmission line failure in the corresponding region under each strong wind scenario in the strong wind scenario sample set; randomly assigning a power system operating condition to each strong wind scenario in each region, and obtaining the operating risk value of the power system under the corresponding operating condition; constructing and training a risk assessment model; and using the model to provide a power system operating risk score assessment.

The invention discloses a generative resource distribution feature mining and dynamic anomaly detection method and related equipment, and the method comprises the steps: carrying out the multi-dimensional attribute data collection of a plurality of heterogeneous business systems, and obtaining an initial resource data set of a target entity; preprocessing the initial resource data set to obtain a standard resource data set; based on the standard resource data set, constructing a multi-dimensional feature vector of each target entity; inputting the multi-dimensional feature vector into a pre-trained anomaly detection model, and outputting an anomaly score of each target entity; and marking the abnormal target entity according to the abnormality score to obtain an abnormality analysis report. The method can improve the recognition precision of the abnormal data, and can be widely applied to the technical field of data processing.

The invention relates to the technical field of composite material performance prediction, and discloses a composite material flame retardant property and thermal stability prediction method based on thermogravimetric analysis and infrared spectrum data fusion. The invention aims to solve the problem that it is difficult to effectively fuse multi-modal data to realize quantitative prediction in the prior art. The method comprises the following steps: firstly, collecting thermogravimetric and infrared spectrum combined data, calculating gas transmission lag time, and carrying out forward correction on infrared spectrum data to realize time sequence alignment; then solid-phase thermogravimetric feature vectors and gas-phase infrared feature vectors are extracted, and a fusion feature matrix is generated; extracting solid-phase and gas-phase characteristics respectively by using a dual-channel LSTM network, and performing weighted fusion based on a self-attention mechanism; and finally, synchronously outputting a flame retardant index predicted value and a flame retardant grade classification result through numerical mapping and classification judgment. According to the method, multi-dimensional feature mining of the whole pyrolysis process of the material is realized, and a rapid and accurate digital evaluation means is provided for screening of the flame-retardant material.

The invention relates to an artificial intelligence-based prestress tensioning process anomaly identification method, which specifically comprises the following steps of: annularly and symmetrically arranging four steel beams on a bridge main tower for synchronous tensioning, installing a sensor network on tensioning equipment of each steel beam, synchronously acquiring data to construct a sample for training, and labeling the sample; constructing an anomaly recognition model, inputting sample data into the model, obtaining an anomaly recognition result through composite embedding and space-time position coding of tensioning time sequence data, a multi-stage dynamic feature modulation and anomaly sensitive feature mining module and a multi-stage space-time anomaly detector, calculating a total loss function of the model, and training the anomaly recognition model; and deploying a sensor network in a to-be-recognized target tensioning process to collect related data, inputting the related data into the trained anomaly recognition model, and outputting an anomaly classification result. According to the invention, multiple types of pre-stress tension anomalies can be accurately identified, and the detection efficiency and accuracy are improved.

The invention relates to the technical field of computer vision and target detection, and discloses a small target detection network for adaptive fine-grained feature mining. The invention provides a network, and the network overcomes the contradiction between the calculation efficiency and the detection precision of a traditional method through collaborative design of adaptive fine-grained feature mining and RoI feature interaction. The network can detect a high-resolution image by using difficult areas in a conventional-level feature map and a high-resolution shallow-layer feature map at the same time, the difficult areas with dense information are automatically positioned through a foreground probability discriminator, background redundancy calculation is avoided by using an iterative mining strategy, and the small target detection speed is increased; meanwhile, the Inter-RoI feature interaction module realizes bidirectional complementary enhancement of deep semantics and shallow details in a key difficult area, and in combination with a high-resolution detection head and a result fusion mechanism, the feature characterization capability of a small target can be enhanced under a complex background, and finally, the feature characterization capability of the small target can be enhanced while the high reasoning efficiency is kept. And the detection precision and robustness of small targets which are non-uniformly distributed and have weak features in the aerial image are improved.

The invention relates to the technical field of cylindrical batteries, in particular to a cylindrical battery fault prediction method, which comprises five steps of data acquisition and preprocessing, multi-dimensional time sequence feature construction, LSTM prediction model construction, model training and optimization and fault type and probability output. According to the cylindrical battery fault prediction method, three types of time sequence features are constructed, differential operation and information entropy are combined, dynamic signals of battery fault precursor are comprehensively captured, and the problem that a traditional method is single in feature is solved; a double-branch LSTM architecture is adopted, a classification branch introduces an attention mechanism to highlight key time sequence information, a regression branch optimizes long sequence training through residual connection, and the time sequence feature mining capability is improved; dual output of fault type identification and probability prediction is realized, faults are warned in advance, and the problem of prediction lag is solved.

The invention belongs to the technical field of water quality parameter inversion, and particularly relates to a machine learning-based suspended matter concentration remote sensing inversion method and system, and the method comprises the steps: collecting a satellite remote sensing image and synchronous actual measurement suspended matter concentration data, and obtaining a space gridding water body remote sensing reflectivity matrix through radiometric calibration, atmospheric correction and water body mask. Reconstructing and denoising through wavelet decomposition, and normalizing and standardizing the spectrum to obtain a spectrum numerical sequence; multi-scale waveband combination and differential features are constructed based on the sequence, and sensitive features are screened out by using XGBoost. A physical constraint term is constructed in combination with sensitive characteristics and a water body radiation transmission rule, and an intermediate inversion result is obtained through numerical iteration. And performing deviation compensation on an intermediate result by using a deep learning residual error, and finally performing spatial smoothing, consistency verification and high-concentration saturation optimization to obtain a high-precision and spatially continuous suspended matter concentration spatial distribution result. According to the method, efficient feature mining and physical mechanism deep fusion are realized, and the explanatory and generalization ability of the model is greatly improved.

The invention discloses a cognitive resident node recognition and attention link construction method, device and system, and belongs to the technical field of user behavior modeling, and the method comprises the steps: user behavior data collection, structured processing and preprocessing; behavior chain construction and semantic alignment: unifying behavior semantic description and constructing a time sequence mapping chain by identifying a behavior stage, and providing path input for cognitive resident node identification and attention link construction; recognizing cognitive resident nodes and constructing attention links, recognizing cognitive resident points of a user in corresponding stages, and establishing flow paths of attention between different nodes and behavior stages to form a structured attention link map. The user behavior modeling method solves the problems of data dimension splitting, behavior chain missing, rough intention modeling and insufficient time sequence evolution characteristic mining in an existing user behavior modeling method.

The invention relates to the technical field of river and lake health diagnosis and evaluation, in particular to an intelligent river and lake health diagnosis and evaluation system and method fusing remote sensing and monitoring data, and the method comprises the steps: firstly obtaining remote sensing image data and ground monitoring data, and constructing a multi-source fusion data set through space-time registration and scale normalization processing; based on an index system covering five dimensions of hydrology, water quality and the like, calling a corresponding algorithm model to calculate and standardize health indexes; then, through dual-channel collaborative diagnosis formed by a rule inference engine and a depth feature mining model, a diagnosis result is optimized in combination with a bidirectional feedback mechanism; and finally, fusing the two diagnosis results by adopting an evidence theory algorithm to generate a comprehensive evaluation index, a health level and a problem diagnosis conclusion, and associating spatial geographic information to generate an evaluation map. According to the invention, accurate diagnosis and visual presentation of river and lake health are realized, and scientific support is provided for river and lake management and restoration.

The invention relates to a terminal protection and baseline inspection method and device based on a complex asset network, and the method comprises the steps: constructing a multi-dimensional holographic network topological graph, and determining the position association of a terminal; the method comprises the steps of semantic analysis of data, risk feature mining and database establishment, generation of an intelligent protection strategy according to a terminal role, a data sensitive level and risk assessment, generation of a dynamically adjustable baseline template by summarizing data, real-time monitoring of a terminal during operation, intelligent check by comparing the template and the strategy, rapid early warning of abnormity, and finally feedback of monitoring data. And optimizing a risk library, a strategy and a template. According to the scheme, the risk can be accurately identified, efficient protection and inspection are realized, and the security of the complex asset network terminal is improved.

The invention discloses a highway intelligent risk monitoring method and system based on multi-modal information, and the method comprises the steps: collecting multi-modal traffic data, constructing a road network holographic portrait through a road network holographic portrait risk prediction model, positioning a potential risk point through a dual time-space mask anomaly detection model, and carrying out the detection of the potential risk point. Lane-level risk assessment is completed by means of a lane-level risk dynamic assessment algorithm, and risk information is output after multi-dimensional fusion analysis is carried out through a traffic risk intelligent research and judgment platform. All the models and algorithms are operated cooperatively, data integration, feature mining, anomaly recognition, risk assessment and fusion research and judgment are achieved step by step, a whole-process monitoring system from data collection to result output is constructed, refined and dynamic risk monitoring from the global road network to the local lane is achieved, the risk recognition accuracy and monitoring comprehensiveness are effectively improved, and the risk monitoring efficiency is improved. And technical support is provided for safe operation of the expressway.